Bleeds can present complex and irregular shapes. However, h-Shap explores fixed quad-trees of the input image. Thus, we extend the original implementation with standard ideas of cycle spinning [1]}. Denote \(s\)  the minimal feature size in h-Shap (i.e., the size of the smallest leaf explored by the algorithm), and let \(\mathbf {\rho } = \lbrace \rho _i\rbrace _{i=1}^{n_{\rho }}\)  be \(n_{\rho }\)  equally spaced radii between 0 and \(s\) , and let \(\mathbf {\alpha } = \lbrace \alpha _i\rbrace _{i=1}^{n_{\alpha }}\)  be \(n_{\alpha }\)  equally spaced angles between 0 and \(2\pi \) . Then, we average the saliency maps obtained by cycle spinning the original partition by the vector \((\rho \cos (\alpha ), \rho \sin (\alpha ))\) , \(\rho \in \mathbf {\rho },~\alpha \in \mathbf {\alpha }\) . Finally, we note that we use the unconditional expectation over the training split of the RSNA dataset to mask features, which is a valid choice in MIL binary classification problems [2]}. In this work, we use h-Shap with an absolute importance tolerance \(\tau \)  equal to 0 (i.e. h-Shap explores all partitions with a positive Shapley coefficient), minimal feature size \(s = 64\) , number of radii \(n_{\rho } = 3\) , and number of angles \(n_{\alpha } = 12\) .
